Process control charts

Q7 PROCESS CHART. PARETO ANALYSIS, CAUSE AND EFFECT DIAGRAM, HISTOGRAM, CORRELATION DIAGRAMS, PROCESS CONTROL CHARTS, CHECK SHEETS... 

Fig. 4.12. Statistical process control chart for TXRF measurement systems. The sensitivity of the system can be controlled by daily calibration with an...

Statistical process control charts (such as the x-bar and range charts) plot measurements as a function of time [Grant and Leavenworth (1988)]. With reference to the current day, what part of these charts approximates an enumerative study What part of these charts approximates an analytic study Are the parts different Are the uses different ... 

Figure 10.2 Statistical process control charts for clearings. Top panel runs chart showing clearings as a function of measurement number. Middle panel x-bar chart with dashed upper control limit (UCL) and lower control limit (LCL) solid horizontal line is the grand mean, X. Bottom panel range chart with dashed upper control limit (UCL) solid horizontal line is the average range, r.

Figure 10.3 Statistical process control charts for issues. See Figure 10.2 for details.

Initial inspection of Figure 10.1 showed what appears to be a cyclical pattern in the clearings and issues. This was confirmed by the statistical process control charts in Figures 10.2-10.4. The broad peaks and valleys in Figure 10.1 seem to repeat every 20 to 25 days. At first, this seemed to be a strange number of days for a cycle - a monthly cycle of 30 or 31 days would have made more sense to some of us. However, the student was quick to point out that the average business month has between 21 and 22 days (365.25 calendar days per year) x (5 business days per week) / (7 calendar days per week) = 260.89 business days per year which, when divided by 12 months, is 21.75 or approximately 22 business days per month. 

Develop a metrics system allowing for quantifiable results wherever possible for example, use statistical process control charts for manufacturing processes and correlating manufacturing deviations with consumer complaint trends. 

FIG. 8-39 Process control chart for the average (daily pH reaidings. 

Tates AA, Louwerse DJ, Smilde AK, Koot GLM, Berndt H, Monitoring a PVC batch process with multivariate statistical process control charts, Industrial and Engineering Chemistry Research, 1999, 38, 4769 1776. 

Figure 3.2 Tailoring process control chart. EMI, Warp extensibility EM2, Weft extensibility THV, Total hand value TAV, Total appearance value.

Pertinent process and product data can be processed by the DAS to yield process control charts and process capability SPC Information Is available real-time... 

Statistical process control charts (SPC charts) are used to plot quality parameter points from samples taken at different times during a run. Even if all of the points are within specifications, when they are plotted on a graph you may see quite clearly that there is a trend that in time will result in off-specification material unless an adjustment is made. An upset or out-of-control situation is both vividly revealed and documented by such a chart (see Figure 16-3). 

FIGURE 17.3 Graph showing a statistical process control chart. 

De Thomas etal. [Ill] studied the production of polyurethanes and showed that NIRS can be used successfully to monitor the course of the reaction in real time. Spectral data were obtained with a dispersive instrument, using standard transflectance probes. An MLR model was derived for the quantitative determination of isocyanate concentrations during the urethane polymerization reaction. Model predictions were used to build statistical process control charts and to detect trends along the polymerization reaction. The authors suggested that the integration of NIRS with process control routines could lead to improvements of product quality and consistency, while minimizing reaction time. However, model predictions were not used as feedback information for any sort of correction of the process trajectory. Similar studies were performed by Dallin [112] for prediction of the acid number during the production of polyesters. 

There are two main facets of statistical quality control. One of them is the use of process control charts for in-process manufacturing operations. These charts, also referred to as variables control charts or attributes control charts, are aimed at evaluating present as well as future performance. The other facet of statistical quality control is acceptance inspection or acceptance sampling. This technique forms the basis for scientihcally evaluating past performance and accepting or rejecting the product. 

In-process quality control serves the basic purpose of providing assurance that the product continues to meet the specified requirements. By employing the process control chart techniques of statistical quality control, we are able to continuously monitor the process and determine whether the process is in or out of control. Patrol or floor inspection gives the inspector an opportunity to verify the visual and dimensional conformity of the processed parts. 

In quality control of production processes, control charts are applied to display results of statistical sampling tests of products. Basic textbooks propose the use of a confidence interval of +/- three standard deviations in this application (see e.g. Wig, 1996). Lor a stable distribution, 99.7 per cent of the results of the tests will fall within this interval. The underlying... 

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